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Kooresh I. Shoghi

Researcher at Washington University in St. Louis

Publications -  69
Citations -  2260

Kooresh I. Shoghi is an academic researcher from Washington University in St. Louis. The author has contributed to research in topics: Medicine & Cancer. The author has an hindex of 20, co-authored 58 publications receiving 1589 citations.

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Co-Clinical Imaging Metadata Information (CIMI) for Cancer Research to Promote Open Science, Standardization, and Reproducibility in Preclinical Imaging

TL;DR: The NCI co-clinical imaging research program (CIRP) conducted a survey to identify metadata requirements for reproducible quantitative coclinical imaging as mentioned in this paper , which has broad implications for capturing coclinical data, enabling interoperability and data sharing, as well as potentially leading to updates to the preclinical Digital Imaging and Communications in Medicine (DICOM).
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Positron Emission Tomography Imaging of Vessel Wall Matrix Metalloproteinase Activity in Abdominal Aortic Aneurysm

TL;DR: In this paper , the authors describe the design, synthesis, characterization, and evaluation in murine AAA and human aortic tissue of a first-in-class MMP-targeted positron emission tomography radioligand, 64Cu-RYM2.
Proceedings ArticleDOI

Artificial tissue bioreactor (ATB) for biological and imaging applications

TL;DR: The development of an artificial tissue bioreactor (ATB) designed to simulate the 3D structure and microenvironment of tissues in vivo, with multiple avenues of sampling, including the tissue chamber, for downstream analysis is reported on.
Journal ArticleDOI

Pilot Study: PARP1 Imaging in Advanced Prostate Cancer

TL;DR: In this paper , the PARP-1 expression in prostate cancer patients with and without HRR genomic alternations using a novel PARPbased imaging agent was assessed by immunohistochemistry (IHC).
Posted ContentDOI

Co-clinical FDG-PET Radiomic Signature in Predicting Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer

TL;DR: In this paper, the authors exploit the heterogeneity afforded by patient-derived tumor xenografts (PDX) to optimize robust radiomic features associated with response to therapy in the context of a co-clinical trial and implement PDX-optimized image features in the corresponding clinical study to predict and assess response using machine-learning (ML) algorithms.